Modern inventory systems, such as those in mail order warehouses, supply chain distribution centers, airport luggage systems, and custom-order manufacturing facilities, face significant challenges in responding to requests for inventory items. As inventory systems grow, the challenges of simultaneously completing a large number of packing, storing, and other inventory-related tasks become non-trivial. In inventory systems tasked with responding to large numbers of diverse inventory requests, inefficient utilization of system resources, including space, equipment, and manpower, can result in lower throughput, unacceptably long response times, an ever-increasing backlog of unfinished tasks, and, in general, poor system performance. Additionally, expanding or reducing the size or capabilities of many inventory systems requires significant changes to existing infrastructure and equipment. As a result, the cost of incremental changes to capacity or functionality may be prohibitively expensive, limiting the ability of the system to accommodate fluctuations in system throughput.
Inventory systems can enhance throughput by efficiently using space and by employing automation, including robotic means to lift and place inventory. One heretofore significant drawback in such automation has been the difficulty that robotic inventory handlers have in manipulating objects that are irregularly shaped, irregularly positioned, or collapsible. For example, known robotic handlers such as a forklift can lift and move objects on pallets, but cannot manipulate objects that are positioned directly on an inventory floor or stacked directly on other objects. By way of another example, known robotic handers that grip an object from the sides cannot manipulate objects that deform when gripped, or objects that are packed closely side-by-side with other objects.